BERTScore [1]} is a text similarity metric that uses BERT [2]} embeddings to find soft matches between word pieces [3]} instead of exact string matching. We introduce a length penalty to favor the candidates that are of similar length to the human reference \(\mathbf {y}\)  and penalize those that deviate from the target compression ratio \(\phi _{\mathbf {y}}\) . \(\lambda \)  defines the extent of penalization and is set to 1 in our experiments. \(\phi _{\mathbf {v}_i}\)  represents the compression ratios of \(\mathbf {v}_i\)  compared to the input \(\mathbf {x}\) . In principle, other similarity metrics can also be used for scoring.
